Executive Summary

Pakistani banks have become efficient after the major reforms in the banking sector. They are relatively more efficient in generating income assets than in generating income. Further improvement in efficiency must come from an increase in the scale of operation in the Pakistani banking sector.

Pakistan commercial banks have improved in terms of efficiency during the decade after the major reforms i.e. from 2001 to 2013.

Total number of branches of Scheduled Commercial Banks in Pakistan have increased to 10,940 as on 31st December, 2013 from 7352 on 30th June, 2001.

The total assets of the scheduled commercial banks increased from 129,31,760.60 million rupees from end June 2012 to 160,86,116.10 million rupees on end June 2013.

The Profit before tax of banks decreased by 8.37% to Rs. 164.4 billion in 2013 from Rs. 179.4 billion in 2012.

Structure and Contemporary State of Banking Sector in Pakistan

Overview

Banking system in Pakistan is divided into four broad categories namely Local Private Banks, Public Sector Commercial Banks, Foreign Banks and Specialized Banks.

There were 38 scheduled banks operating in Pakistan at the end of the financial year 2012-2013

In order to ascertain theworkingefficiency of the banking sector Non-Performing Assets/Loans are the most weighted item on the balance sheet. Pakistan had 3.38 percent Net NPAs of the net advances.

In terms of profitability there has been a decline in the scheduled banks profit mainly due to decline in the net interest margin.

Measuring Technical Efficiency Using DEA

There are parametric as well as non-parametric techniques to measure relative efficiency of firms in an industry. Non Parametric techniques measure the ability of the firms to convert inputs into outputs without taking into account any random event that may have an impact on them. There is no stochastic element in the non-parametric approach. However this approach is popular in literature because of it provides vital diagnostic information in terms of target input and output which can be used for design of business strategy.

We use two basic models input oriented CCR model and BCC model to obtain efficiency scores.

CCR Model

To illustrate CCR model, consider a set of decision making
units (DMUs) n =1, 2,....,j utilizing quantities of inputs X R ∈ + m to produce quantities of outputs Y R ∈ + s . We can denote xij the amount of the ith input used by the DMU j and yrj the amount of the rth output produced by the DMU j. Assuming constant returns-to-scale (CRS), strong disposability of inputs and outputs, and convexity of the production possibility set, the technical efficiency score for the DMU k (denoted by ϴk) can be
obtained by solving following model (Charnes et al., 1978):

Model (1)

min ϴk

Subject to:

Input Constraints: ∑λj xij≤

ϴk xik

Output constraints: ∑λj yrj≥

yrk

λj ≥ 0

where,

λ: Weightage given to inputs and outputs

x: Inputs

y: Output

i: number of inputs

j: number of firms

k: chosen firm

r: number of outputs

The solution to model (1) is interpreted as the largest contraction in inputs of DMU k that can be carried out, given that DMU k will stay within the reference technology. The restrictions i) and ii) form the convex reference technology. The restriction iii) limits the intensity variables to be non-negative. Since the model measures the efficiency of single DMU (i.e., DMU k), it needs to be solved n times to obtain the efficiency score of each DMU in the sample. The optimal value θk* reflects the OTE score of DMU k. OTE measures inefficiencies due to the input/output configuration and as well as the size of operations. This efficiency score is within a range from zero to one, (0, 1) ≤ θk*, with a high score implying a higher efficiency. It is worth mentioning here that the model (1) is an input-oriented model since the objective is to utilize minimum level of inputs with the same level of production.

BCC Model

The CCR model detailed above provide the input-oriented constant returns-to-scale (CRS) envelopment surface, and a measure of overall technical efficiency (θk). Under the assumption of CRS, any scaled-up or scaled-down versions of the input combinations are also included in the production possibility set. However, the constraint over returns-to-scale may be relaxed to allow units to be compared given their scale of operations. To allow returns-to-scale to be variable (i.e., constant, increasing or decreasing), Banker, Charnes and Cooper (1984) added the convexity constraint ∑λj = 1 to the Model (1). Note that the convexity constraint ∑λj = 1, essentially ensures that an inefficient DMU is only ‘benchmarked’ against DMUs of a similar size. The mathematical form of BCC model is as follows:

Model (2)

min ϴkBCC

Subject to

Input Constraints: ∑λj xij≤ϴk xik

Output constraints: ∑λj yrj≥yrk

λj ≥ 0

∑λj = 1

λ: Weightage given to inputs and outputs

x : Inputs

y: Output

i: number of inputs

j: number of firms

k: chosen firm

r: number of outputs

The optimal value of the ϴk (i.e., ϴk*) represents pure technical efficiency which is a measure of efficiency without scale efficiency. We should also note that if a DMU is characterized as efficient in the CCR model, it will also be characterized as efficient with the BCC model. However, the converse is not necessarily true.

Scale Efficiency

Overall Technical efficiency can be decomposed into two multiplicative and mutually exclusive components: Pure Technical Efficiency and Scale Efficiency. While Pure Technical efficiency score in obtained from BCC model for Scale efficiency we use the following formula:

Input Output Specification and Data Storage

Thisstudy is conducted for the post major reform period in the banking sector from 2001 to 2013. The first and the most challenging task in measuring efficiency using DEA is to select and specify the inputs and outputs of banks. In case of banking literature there is no general consensus about the specific inputs and outputs to measure efficiency. Following Leightner and Lovell (1998) and Ataullah et al. (2004) we use two different albeit complementary input-output models forbanks in India and Pakistan.

Model A: Loan based Model. It states that banks incur operating and interest expenses to produce investment and advances.

Model B: Income based Model. It states that banks incur operating and interest expenses to produce a stream of interest and non-interest income.

Our sample includes 23 scheduled banks incorporated inside as well outside Pakistan. The data was selected only for banks which were in operation from 2001 till 31st December 2013. In case of Pakistan there have been many mergers and acquisitions that have taken place during this period. The data for the merged banks has been incorporated as far as possible. For all those banks whose data was not available for any particular year between 2001 and 2013 have been removed. Also in case of negative values for variables they have scaled up accordingly. All the values were uniformly converted in lakhs of rupees to avoid inconsistency. The monetary quantities was deflated by using the appropriate GDP deflator for each year.

Data for scheduled banks in Pakistan are obtained from the website of State Bank of Pakistan. Annual publication Banking Statistics of Pakistan is the source of data from 2001 to 2013.

Empirical Findings

In this section we discuss the OTE, PTE and SE scores obtained by running the two basic DEA models namely, CCR and BCC models. The results of the DEA modelling are derived using the computer program Basic MAX DEA.

Figure 1

Source: Calculated by the author using data from State Bank of Pakistan

Table 1: Loan based model, Efficiency scores for Pakistan

Year

OTE

PTE

SE

2001-2002

0.7742

0.8605

0.8997

2002-2003

0.5337

0.9797

0.5447

2003-2004

0.5912

0.9794

0.6036

2004-2005

0.5751

0.9784

0.5878

2005-2006

0.5669

0.9914

0.5718

2006-2007

0.6187

0.9828

0.6295

2007-2008

0.6049

0.9818

0.6161

2008-2009

0.6586

0.9865

0.6676

2009-2010

0.6969

0.9844

0.7080

2010-2011

0.7420

0.9877

0.7512

2011-2012

0.7499

0.9837

0.7623

2012-2013

0.5827

0.9671

0.6025

2013-2014

0.8978

0.9214

0.9744

Average Score

66%

97%

69%

Inefficiency Score

34%

3%

31%

Source: Calculated by the author using data from State Bank of Pakistan

Table 2: Income based model, Efficiency scores for Pakistan

Year

OTE

PTE

SE

2001-2002

0.3399

0.8476

0.4172

2002-2003

0.4860

0.9747

0.4973

2003-2004

0.5692

0.9641

0.5857

2004-2005

0.5284

0.9609

0.5451

2005-2006

0.5499

0.9837

0.5556

2006-2007

0.5836

0.9762

0.5952

2007-2008

0.6299

0.9784

0.6438

2008-2009

0.5838

0.9635

0.6071

2009-2010

0.5711

0.9509

0.6032

2010-2011

0.6416

0.9625

0.6682

2011-2012

0.6489

0.9668

0.6722

2012-2013

0.6235

0.9767

0.6389

2013-2014

0.5223

0.8417

0.6134

Average Score

56%

95%

59%

Inefficiency Score

44%

5%

41%

Source: Calculated by the author using data from State Bank of Pakistan

In case of Pakistan for the loan based model the average level of inefficiency is 34 percent (Table 1) for 23 banks operating in the country and for the Income based model inefficiency rises to 44 percent (Table 2). In the Pakistani case is in terms of components of technical efficiency whereby the pure technical efficiency is very high and most of the inefficiency due to the scale of operations. There is only 5 percent pure technical inefficiency in Pakistani banks so it is the scale inefficiency which is more than 40 percent that is pulling down the overall efficiency. Our results are consistent with previous studies done by Usman (2010)andNasir (2014).Theyfound that the pure technical efficiency was high due to recent computerization and automation of financial transactions of banks in Pakistan.

The trend of the efficiency in Pakistan banking sector since 2001 can be seen in the figures below:

Conclusion and Recommendations

This paper provides an evidence of technical inefficiency prevailing in the banking sector of Pakistan banks in the 21st century after the implementation of major reforms.

Using the non-parametric DEA it is found that there has been an improvement in the overall technical efficiency.

According to Jaffry S. et al. (2012) Pakistan was slow to adapt to changes but efficiency improved gradually, which is consistent with our results.

According to Ataullah et al. (2004) the improvement in efficiency of Pakistani banks during the 20th century was due to scale efficiency.

The results are indicative of the fact that efficiency is higher in the case using earning assets as output i.e. Loan Based Model. It is argued that even when banks are becoming more efficient in increasing the quantity of loans, advances and investments, this efficiency is not being translated into higher efficiency in generating income.

Pakistan’s case improvement should mainly come from the scale efficiency. State Bank of Pakistan’s governor Yasir Anwar (Speech at Bahria University, Karachi on 22nd December 2014) has also acknowledged this fact:

“…the existing branch network of the banking system is insufficient to serve the millions of unbanked masses. The total number of bank branches in Pakistan is very low at around 10,600 which place Pakistan among the countries with the highest per bank population of around 15,000 persons per branch.”

In case of developing countries Non-Performing Assets/Loans are an important reason for inefficiency mainly due to increased lending to the priority sectors, which have a low rate of return. NPAs are also driven by political factors, weakness of the legal system to recover such loans and several other factors not in the hands of managers. With the recent implementation of the Basel III norms in both the countries, improvement in efficiency of the banking
sector is widely believed in the coming years.

Executive Summary

Pakistani banks have become efficient after the major reforms in the banking sector. They are relatively more efficient in generating income assets than in generating income. Further improvement in efficiency must come from an increase in the scale of operation in the Pakistani banking sector.

Pakistan commercial banks have improved in terms of efficiency during the decade after the major reforms i.e. from 2001 to 2013.

Total number of branches of Scheduled Commercial Banks in Pakistan have increased to 10,940 as on 31st December, 2013 from 7352 on 30th June, 2001.

The total assets of the scheduled commercial banks increased from 129,31,760.60 million rupees from end June 2012 to 160,86,116.10 million rupees on end June 2013.

The Profit before tax of banks decreased by 8.37% to Rs. 164.4 billion in 2013 from Rs. 179.4 billion in 2012.

Structure and Contemporary State of Banking Sector in Pakistan

Overview

Banking system in Pakistan is divided into four broad categories namely Local Private Banks, Public Sector Commercial Banks, Foreign Banks and Specialized Banks.

There were 38 scheduled banks operating in Pakistan at the end of the financial year 2012-2013

In order to ascertain theworkingefficiency of the banking sector Non-Performing Assets/Loans are the most weighted item on the balance sheet. Pakistan had 3.38 percent Net NPAs of the net advances.

In terms of profitability there has been a decline in the scheduled banks profit mainly due to decline in the net interest margin.

Measuring Technical Efficiency Using DEA

There are parametric as well as non-parametric techniques to measure relative efficiency of firms in an industry. Non Parametric techniques measure the ability of the firms to convert inputs into outputs without taking into account any random event that may have an impact on them. There is no stochastic element in the non-parametric approach. However this approach is popular in literature because of it provides vital diagnostic information in terms of target input and output which can be used for design of business strategy.

We use two basic models input oriented CCR model and BCC model to obtain efficiency scores.

CCR Model

To illustrate CCR model, consider a set of decision making
units (DMUs) n =1, 2,....,j utilizing quantities of inputs X R ∈ + m to produce quantities of outputs Y R ∈ + s . We can denote xij the amount of the ith input used by the DMU j and yrj the amount of the rth output produced by the DMU j. Assuming constant returns-to-scale (CRS), strong disposability of inputs and outputs, and convexity of the production possibility set, the technical efficiency score for the DMU k (denoted by ϴk) can be
obtained by solving following model (Charnes et al., 1978):

Model (1)

min ϴk

Subject to:

Input Constraints: ∑λj xij≤

ϴk xik

Output constraints: ∑λj yrj≥

yrk

λj ≥ 0

where,

λ: Weightage given to inputs and outputs

x: Inputs

y: Output

i: number of inputs

j: number of firms

k: chosen firm

r: number of outputs

The solution to model (1) is interpreted as the largest contraction in inputs of DMU k that can be carried out, given that DMU k will stay within the reference technology. The restrictions i) and ii) form the convex reference technology. The restriction iii) limits the intensity variables to be non-negative. Since the model measures the efficiency of single DMU (i.e., DMU k), it needs to be solved n times to obtain the efficiency score of each DMU in the sample. The optimal value θk* reflects the OTE score of DMU k. OTE measures inefficiencies due to the input/output configuration and as well as the size of operations. This efficiency score is within a range from zero to one, (0, 1) ≤ θk*, with a high score implying a higher efficiency. It is worth mentioning here that the model (1) is an input-oriented model since the objective is to utilize minimum level of inputs with the same level of production.

BCC Model

The CCR model detailed above provide the input-oriented constant returns-to-scale (CRS) envelopment surface, and a measure of overall technical efficiency (θk). Under the assumption of CRS, any scaled-up or scaled-down versions of the input combinations are also included in the production possibility set. However, the constraint over returns-to-scale may be relaxed to allow units to be compared given their scale of operations. To allow returns-to-scale to be variable (i.e., constant, increasing or decreasing), Banker, Charnes and Cooper (1984) added the convexity constraint ∑λj = 1 to the Model (1). Note that the convexity constraint ∑λj = 1, essentially ensures that an inefficient DMU is only ‘benchmarked’ against DMUs of a similar size. The mathematical form of BCC model is as follows:

Model (2)

min ϴkBCC

Subject to

Input Constraints: ∑λj xij≤ϴk xik

Output constraints: ∑λj yrj≥yrk

λj ≥ 0

∑λj = 1

λ: Weightage given to inputs and outputs

x : Inputs

y: Output

i: number of inputs

j: number of firms

k: chosen firm

r: number of outputs

The optimal value of the ϴk (i.e., ϴk*) represents pure technical efficiency which is a measure of efficiency without scale efficiency. We should also note that if a DMU is characterized as efficient in the CCR model, it will also be characterized as efficient with the BCC model. However, the converse is not necessarily true.

Scale Efficiency

Overall Technical efficiency can be decomposed into two multiplicative and mutually exclusive components: Pure Technical Efficiency and Scale Efficiency. While Pure Technical efficiency score in obtained from BCC model for Scale efficiency we use the following formula:

Input Output Specification and Data Storage

Thisstudy is conducted for the post major reform period in the banking sector from 2001 to 2013. The first and the most challenging task in measuring efficiency using DEA is to select and specify the inputs and outputs of banks. In case of banking literature there is no general consensus about the specific inputs and outputs to measure efficiency. Following Leightner and Lovell (1998) and Ataullah et al. (2004) we use two different albeit complementary input-output models forbanks in India and Pakistan.

Model A: Loan based Model. It states that banks incur operating and interest expenses to produce investment and advances.

Model B: Income based Model. It states that banks incur operating and interest expenses to produce a stream of interest and non-interest income.

Our sample includes 23 scheduled banks incorporated inside as well outside Pakistan. The data was selected only for banks which were in operation from 2001 till 31st December 2013. In case of Pakistan there have been many mergers and acquisitions that have taken place during this period. The data for the merged banks has been incorporated as far as possible. For all those banks whose data was not available for any particular year between 2001 and 2013 have been removed. Also in case of negative values for variables they have scaled up accordingly. All the values were uniformly converted in lakhs of rupees to avoid inconsistency. The monetary quantities was deflated by using the appropriate GDP deflator for each year.

Data for scheduled banks in Pakistan are obtained from the website of State Bank of Pakistan. Annual publication Banking Statistics of Pakistan is the source of data from 2001 to 2013.

Empirical Findings

In this section we discuss the OTE, PTE and SE scores obtained by running the two basic DEA models namely, CCR and BCC models. The results of the DEA modelling are derived using the computer program Basic MAX DEA.

Figure 1

Source: Calculated by the author using data from State Bank of Pakistan

Table 1: Loan based model, Efficiency scores for Pakistan

Year

OTE

PTE

SE

2001-2002

0.7742

0.8605

0.8997

2002-2003

0.5337

0.9797

0.5447

2003-2004

0.5912

0.9794

0.6036

2004-2005

0.5751

0.9784

0.5878

2005-2006

0.5669

0.9914

0.5718

2006-2007

0.6187

0.9828

0.6295

2007-2008

0.6049

0.9818

0.6161

2008-2009

0.6586

0.9865

0.6676

2009-2010

0.6969

0.9844

0.7080

2010-2011

0.7420

0.9877

0.7512

2011-2012

0.7499

0.9837

0.7623

2012-2013

0.5827

0.9671

0.6025

2013-2014

0.8978

0.9214

0.9744

Average Score

66%

97%

69%

Inefficiency Score

34%

3%

31%

Source: Calculated by the author using data from State Bank of Pakistan

Table 2: Income based model, Efficiency scores for Pakistan

Year

OTE

PTE

SE

2001-2002

0.3399

0.8476

0.4172

2002-2003

0.4860

0.9747

0.4973

2003-2004

0.5692

0.9641

0.5857

2004-2005

0.5284

0.9609

0.5451

2005-2006

0.5499

0.9837

0.5556

2006-2007

0.5836

0.9762

0.5952

2007-2008

0.6299

0.9784

0.6438

2008-2009

0.5838

0.9635

0.6071

2009-2010

0.5711

0.9509

0.6032

2010-2011

0.6416

0.9625

0.6682

2011-2012

0.6489

0.9668

0.6722

2012-2013

0.6235

0.9767

0.6389

2013-2014

0.5223

0.8417

0.6134

Average Score

56%

95%

59%

Inefficiency Score

44%

5%

41%

Source: Calculated by the author using data from State Bank of Pakistan

In case of Pakistan for the loan based model the average level of inefficiency is 34 percent (Table 1) for 23 banks operating in the country and for the Income based model inefficiency rises to 44 percent (Table 2). In the Pakistani case is in terms of components of technical efficiency whereby the pure technical efficiency is very high and most of the inefficiency due to the scale of operations. There is only 5 percent pure technical inefficiency in Pakistani banks so it is the scale inefficiency which is more than 40 percent that is pulling down the overall efficiency. Our results are consistent with previous studies done by Usman (2010)andNasir (2014).Theyfound that the pure technical efficiency was high due to recent computerization and automation of financial transactions of banks in Pakistan.

The trend of the efficiency in Pakistan banking sector since 2001 can be seen in the figures below:

Conclusion and Recommendations

This paper provides an evidence of technical inefficiency prevailing in the banking sector of Pakistan banks in the 21st century after the implementation of major reforms.

Using the non-parametric DEA it is found that there has been an improvement in the overall technical efficiency.

According to Jaffry S. et al. (2012) Pakistan was slow to adapt to changes but efficiency improved gradually, which is consistent with our results.

According to Ataullah et al. (2004) the improvement in efficiency of Pakistani banks during the 20th century was due to scale efficiency.

The results are indicative of the fact that efficiency is higher in the case using earning assets as output i.e. Loan Based Model. It is argued that even when banks are becoming more efficient in increasing the quantity of loans, advances and investments, this efficiency is not being translated into higher efficiency in generating income.

Pakistan’s case improvement should mainly come from the scale efficiency. State Bank of Pakistan’s governor Yasir Anwar (Speech at Bahria University, Karachi on 22nd December 2014) has also acknowledged this fact:

“…the existing branch network of the banking system is insufficient to serve the millions of unbanked masses. The total number of bank branches in Pakistan is very low at around 10,600 which place Pakistan among the countries with the highest per bank population of around 15,000 persons per branch.”

In case of developing countries Non-Performing Assets/Loans are an important reason for inefficiency mainly due to increased lending to the priority sectors, which have a low rate of return. NPAs are also driven by political factors, weakness of the legal system to recover such loans and several other factors not in the hands of managers. With the recent implementation of the Basel III norms in both the countries, improvement in efficiency of the banking
sector is widely believed in the coming years.